Root cause analysis of manufacturing variation from optical scanning data

Anh Tuan Bui ()
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Anh Tuan Bui: Virginia Commonwealth University

Annals of Operations Research, 2024, vol. 339, issue 1, No 5, 130 pages

Abstract: Abstract Identifying the root causes of part-to-part variation is a central problem in most six-sigma programs, especially of modern manufacturing processes. This is challenging as the sources and patterns of the variation are often unknown or previously unidentified. A small literature aims to address this problem by discovering unknown, previously unidentified variation sources, in a manner that helps understand their nature, from only a sample of measurement data. However, the common solution of this literature is unideal for this objective in terms of both methodology and metrology aspects. This paper proposes a convolutional generative modeling framework for optical scanning data to address this limitation. The proposed approach can correctly discover the true variation sources and visualize their individual patterns in two manufacturing examples, without any prior knowledge of the variation. The approach also outperforms state-of-the-art methods in these examples.

Keywords: Adversarial autoencoder; Convolutional neural networks; Generative adversarial networks (GAN); Phase I analysis; Statistical process control; Variation reduction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-05077-5

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